The (Alpha, Kappa, Gama) parametrisation allows to easily compare several well known priors.
We already mentioned that the Dirichlet process can be obtained by setting Gama = 0, the normalised inverse Gaussian process by setting Alpha = 1, Gama = 1/2 and the normalised stable process by setting Alpha = 1, Kappa = 0.
The stable process is a convenient model because its parameter \(\gamma \)  has a simple interpretation: it can be used to tune how informative the prior on the number of components is.
Small values of Gama bring the process closer to a Dirichlet process, where the prior on the number of components is a relatively peaked distribution around \(\alpha \log n\) .
In contrast, the larger the value of Gama is, the flatter the distribution is.
More guidelines on how to choose the parameters may be found in [1]}, notably by considering the expected prior number of components.
The expected prior number of components for normalised generalised gamma processes is not trivial to compute due to numerical instabilities, but we provide functions to compute prior distribution on the number of clusters for the normalised stable process and for the Dirichlet process.
These functions require installing the packages gmp and Rmpfr for Multiple Precision Arithmetic, both available on CRAN.
